Geometric Basis of Action Potential of Skeletal Muscle Cells

Although we know something about single cell neuromuscular junction, It is still mysterious how multiple skeletal muscle cells coordinate to complete the intricate spatial curve movement. Here I propose a hypothesis that skeletal muscle cell populations with action potentials are alligned according to a curved manifolds on space(a curved shape on space) and the skeletal muscle also moves according to this corresponding shape(manifolds) when an specific motor nerve impulses are transmitted. the action potential of motor nerve fibers has the characteristics of time curve manifold and this time manifold curve of motor nerve fibers come from visual cortex in which a spatial geometric manifolds are formed within the synaptic connection of neurons. This spatial geometric manifolds of the synaptic connection of neurons orginate from spatial geometric manifolds in outside nature that are transmitted to brain through the cone cells and ganglion cells of the retina.Further,the essence of life is that life is an object that can move autonomously and the essence of life's autonomous movement is the movement of proteins. theoretically, due to the infinite diversity of geometric manifold shapes in nature, the arrangement and combination of 20 amino acids should have infinite diversity, and the geometric manifold formed by protein three-dimensional spatial structure should also have infinite diversity.

Stochastic Memristive Interface for Neural Signal Processing

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.

Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.

Simulation scalability of large brain neuronal networks thanks to time asynchrony

We present here a new algorithm based on a random model for simulating efficiently large brain neuronal networks. Model parameters (mean firing rate, number of neurons, synaptic connection probability and postsynaptic duration) are easy to calibrate further on real data experiments. Based on time asynchrony assumption, both computational and memory complexities are proved to be theoretically linear with the number of neurons. These results are experimentally validated by sequential simulations of millions of neurons and billions of synapses in few minutes on a single processor desktop computer.

GABAB receptor auxiliary subunits modulate Cav2.3-mediated release from medial habenula terminals

The synaptic connection from medial habenula (MHb) to interpeduncular nucleus (IPN) is critical for emotion-related behaviors, and uniquely expresses R-type Ca2+ channels (Cav2.3) and auxiliary GABAB receptor (GBR) subunits, the K+-channel tetramerization domain-containing proteins (KCTDs). Activation of GBRs facilitates or inhibits transmitter release from MHb terminals depending on the IPN subnucleus, but the role of KCTDs is unknown. We therefore examined the localization and function of Cav2.3, GBRs, and KCTDs in this pathway in mice. We show in heterologous cells that KCTD8 and KCTD12b directly bind to Cav2.3 and that KCTD8 potentiates Cav2.3 currents in the absence of GBRs. In the rostral IPN, KCTD8, KCTD12b and Cav2.3 co-localize at the presynaptic active zone. Genetic deletion indicated a bidirectional modulation of Cav2.3-mediated release by these KCTDs with a compensatory increase of KCTD8 in the active zone in KCTD12b-deficient mice. The interaction of Cav2.3 with KCTDs therefore scales synaptic strength independent of GBR activation.

Costs and benefits of using rhythmic rate codes

Neural oscillations are one of the most well-known macroscopic phenomena observed in the nervous system, and the benefits of oscillatory coding have been the topic of frequent analysis. Many of these studies focused on communication between populations which were already oscillating, and sought to understand how synchrony and communication interact. In this paper, take an alternative approach. We focus on measuring the costs, and benefits, of moving to an from an aperiodic code to a rhythmic one. We utilize a Linear-Nonlinear Poisson model, and assume a rate code. We report that no one factor seems to predict the costs, or benefits, of translating into a rhythmic code. Instead the synaptic connection type, strength, population size, and stimulus and oscillation firing rates interact in nonlinear ways. We suggest a number of experiments that might be used to confirm these predictions.Author summaryIt’s good to oscillate, sometimes.

Propagation of spiking regularity in feedforward networks with recurrent connections

The regularity of the inter-spike intervals (ISIs) gives a critical window into how the information is coded temporally in the cortex. Previous researches mostly adopt pure feedforward networks (FFNs) to study how the network structure affects spiking regularity propagation, which ignore the role of local dynamics within the layer. In this paper, we construct an FFN with recurrent connections and investigate the propagation of spiking regularity. We argue that an FFN with recurrent connections serves as a basic circuit to explain that the regularity increases as spikes propagate from middle temporal visual areas to higher cortical areas. We find that the reduction of regularity is related to the decreased complexity of the shared activity co-fluctuations. We show in simulations that there is an appropriate excitation–inhibition ratio maximizing the regularity of deeper layers. Furthermore, it is demonstrated that collective temporal regularity in deeper layers exhibits resonance-like behavior with respect to both synaptic connection probability and synaptic weight. Our work provides a critical link between cortical circuit structure and realistic spiking regularity.

Natural Cinnamaldehyde and Its Derivatives Ameliorate Neuroinflammatory Pathways in Neurodegenerative Diseases

Neurodegenerative diseases are devastating and incurable disorders characterized by neuronal dysfunction. The major focus of experimental and clinical studies are conducted on the effects of natural products and their active components on neurodegenerative diseases. This review will discuss an herbal constituent known as cinnamaldehyde (CA) with the neuroprotective potential to treat neurodegenerative disorders, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Accumulating evidence supports the notion that CA displays neuroprotective effects in AD and PD animal models by modulating neuroinflammation, suppressing oxidative stress, and improving the synaptic connection. CA exerts these effects through its action on multiple signaling pathways, including TLR4/NF-κB, NLRP3, ERK1/2-MEK, NO, and Nrf2 pathways. To summarize, CA and its derivatives have been shown to improve pathological changes in AD and PD animal models, which may provide a new therapeutic option for neurodegenerative interventions. To this end, further experimental and clinical studies are required to prove the neuroprotective effects of CA and its derivatives.